In traditional models like linear regression and ANOVA, assumptions such as linearity, independence of errors, homoscedasticity, and normality of residuals are foundational.

Model-combining (i.e., mixing) methods have been proposed in recent years to deal with uncertainty in model selection. Even though advantages of model combining over model selection have been ...

We introduce a fast stepwise regression method, called the orthogonal greedy algorithm (OGA), that selects input variables to enter a p-dimensional linear regression model (with p ≫ n, the sample size ...

Linear regression and feature selection are two such foundational topics. Linear regression is a powerful technique for predicting numbers from other data.

Discover how linear regression works, from simple to multiple linear regression, with step-by-step examples, graphs and real-world applications.

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using JavaScript. Linear regression is the simplest machine learning technique to predict a single numeric value, ...

The simplest form of regression in Python is, well, simple linear regression. With simple linear regression, you're trying to ...